simulation of carbonated water injection (cwi), challenges ... · pdf filedevex 2014 -...

DEVEX 2014 - Aberdeen

Simulation of Carbonated Water Injection

(CWI), Challenges and Solution

Foroozesh J., Jamiolahmady M. (Jami) , Sohrabi M.

Presented by: Jalal Foroozesh

1


Outline

• Introduction

• Objectives

• Our modelling approach

• Governing equations.

• GA optimisation tool.

• Experimental conditions.

• Simulation results and discussions

• Summary and conclusions

2


Introduction - 1

• CWI is an augmented water injection (WI) process

with great EOR potentials.

• During CWI, CO2 is transferred from water into oil

due to its higher CO2 solubility, resulting in higher

oil recovery.

3

Carbonated Water

(CO2 + Water)Oil

CO2


Introduction - 2

• CWI can be used when limited source of CO2 isavailable.

• CO2 is in solution rather than free, so less risk of earlygas breakthrough (EOR aspect).

• A good technique for safe CO2 storage with less risk ofleakage (Environmental aspect).

• Compared to experimental investigation of the subject,mathematical modelling of CWI has received lessattention.

4


Introduction - 3

• Viscosity reduction, oil swelling and

wettability alteration are among the main

mechanisms giving more oil recovery during

CWI.

• It is believed that compositional simulation of

the process better captures the process.

• Current compositional reservoir simulators are

based on instantaneous equilibrium assumption.

5


Introduction - 4

• Our experimental studies have shown that CO2

transfer from water to oil is a slow and non-

equilibrium based process during CWI.

• Therefore this multi-physics (fluid flow and

mass transfer) process can not be simulated

realistically using available simulators.

6


Objectives

• Mathematical modelling/numerical simulation

of CWI Process.

• Develop a compositional simulator that captures

CWI multi-physics process realistically.

• The simulator needs to be based on non-

equilibrium conditions by capturing the kinetics of

mass transfer.

7


Governing Equations

8

𝝋𝝏(𝝆𝒐𝒔𝒐𝝎𝒐

𝒐)

𝝏𝒕= −𝜵 𝝆𝒐𝒖𝒐𝝎𝒐

𝒐 𝟏 𝝋𝝏 𝝆𝒐𝒔𝒐𝝎𝒐

𝒄𝒐𝟐

𝝏𝒕= −𝜵 𝝆𝒐𝒖𝒐𝝎𝒐

𝒄𝒐𝟐 + 𝑼 (𝟐)

𝝋𝝏(𝝆𝒘𝒔𝒘𝝎𝒘

𝒘)

𝝏𝒕= −𝜵 𝝆𝒘𝒖𝒘𝝎𝒘

𝒘 𝟑 𝝋𝝏(𝝆𝒘𝒔𝒘𝝎𝒘

𝒄𝒐𝟐)

𝝏𝒕= −𝜵 𝝆𝒘𝒖𝒘𝝎𝒘

𝒄𝒐𝟐 − 𝑼 𝟒

• Mass transfer term (U=f(concentration, overall mass transfer coefficient, Sw))controls the amount and the rate of CO2 transfer from water to oil phase.

• Equations are solved for P, sw, 𝑤𝑤𝑐𝑜2, 𝑤𝑜

𝑐𝑜2.

• Fully implicit method was used to solve the coupled equations.

• Viscosity and density change due to CO2 transfer between oil and water phases.


Genetic Algorithm (GA)

• Mass transfer coefficient (MTC) and Kr wereunknown in our CWI simulation.

• In our approach these unknown parameters aredetermined by history matching the TOP and DPexperimental data.

• To achieve this, a GA based optimization programwas developed that could be linked to oursimulator.

9


Core Flooding Experimental Conditions

• Water-wet ( WI and CWI )

• Mixed-Wet ( WI and CWI )

• Decane with well defined properties used as

oil in these tests conducted at 2500 psi.

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Water + Decane + CO2fully saturated with Decane

(Swi=0)

CW

q=20 cc/hr

Clashach Core:

WW: k=850 mD, PV=120 cc

MW: k=1 D, PV=180 cc

CW CO2 content=5%

CW-C10, Partition coefficient=3-4


Simulations Results and Discussions

11


In-house Simulator - Consistency Check

• Water injection (WI) was simulated first usingour simulator under its black oil mode.

• A Kro-w curve that simulates the WI test wasobtained by history matching of thecorresponding production data using GA.

• Results of our simulator were compared tothose of E100 demonstrating good agreement.

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Water-Wet Core - 1

13

WI and CWI WI


Water-Wet Core - 2

14

Tuned MTC value of 1.5 E-5 with oil-water Kr.

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20

TO

P(c

c)

Time(hr)

Exp

Model

CWI


Mixed-Wet Core - 1

15


Mixed-Wet Core - 2

16


Summary and Conclusions - 1

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• A compositional based simulator has been developed.

• The simulator is based on non-equilibrium assumption and

captures the kinetics of CO2 transfer.

• A Genetic Algorithm (GA) based optimization program has

been developed.

• Kr and MTC can be tuned by GA.

• WI or CWI core flood tests of water-wet and mixed-wet cores

when they were fully saturated with Decane were simulated.


Summary and Conclusions - 2

18

• WI tests were simulated first and later the results were used for

the simulation of the corresponding CWI tests.

• For water-wet cores, water-oil kr curve obtained was used and

only MTC was tuned to match CWI production data.

• Main mechanism: Swelling.

• For mixed-wet cores, kr curve and MTC were tuned to match

CWI production data.

• Main mechanism: Wettability alteration and Swelling.


Acknowledgement

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• The support of the following industrial partners acknowledged.

• The UK Department of Energy and Climate Change (DECC),

• Petrobras,

• Total Exploration and Production,

• ADCO,

• BG Group, and

• Galp Energia.


Thank you for your attentions

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